# Crossref Recent Publications by Query (`v0iddo/crossref-recent-publications`) Actor For each query, pull recent works from Crossref's public REST API with a server-side date filter. One row per work — DOI, title, authors, type, container, ISSN, abstract, refs, citations, license, funders. Covers all academia (humanities, social sci, engineering, business). No auth. - **URL**: https://apify.com/v0iddo/crossref-recent-publications.md - **Developed by:** [vøiddo](https://apify.com/v0iddo) (community) - **Categories:** Business, News - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing $0.20 / 1,000 crossref publication extracteds This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event ## What's an Apify Actor? Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours, and optionally produces a well-defined JSON output, datasets with results, or files in key-value store. In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server. Actors are written with capital "A". ## How to integrate an Actor? If asked about integration, you help developers integrate Actors into their projects. You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready. The best way to integrate Actors is as follows. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md). If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md). For usage examples, see the [API](#api) section below. For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt). # README ## Crossref Recent Publications by Query For each query, pull recently-published works from Crossref's public DOI registry. One row per work, with a server-side date filter so even narrow daily windows are fast. ### What you get ```` { "doi": "10.1145/3674892.3675012", "title": "Differentially Private In-Context Learning", "type": "proceedings-article", "publisher": "Association for Computing Machinery", "container": "Proceedings of the 31st ACM SIGKDD Conference", "shortContainer":"KDD '26", "issn": \["2153-1234"], "authors": \["Alice Doe", "Bob Smith"], "authorCount": 2, "published": "2026-05-30", "created": "2026-06-01", "abstract": "We study DP-SGD applied to ICL...", "subject": \["Computer Science"], "referenceCount": 42, "isReferencedByCount": 0, "licenseUrl": "https://www.acm.org/publications/policies/...", "funders": \["NSF", "DARPA"], "url": "https://doi.org/10.1145/3674892.3675012", "query": "differential privacy LLM" } ```` ### Why this matters PubMed covers biomedical only. arXiv covers preprints in physics, CS, math, q-bio only. **Crossref covers everything else** — humanities, social sciences, business, law, ecology, materials science — wherever a DOI is registered. A single query gets you the new-works feed across all of academia. ### How to use **Input.** A list of search queries plus a date window in days. The date filter runs server-side (no over-fetch), so a 1-day window costs only as much compute as a 1-day window. ```json { "queries": ["large language models", "diffusion models"], "sinceDays": 7, "dateField": "published", "maxResultsPerQuery": 1000, "typesFilter": [] } ```` **dateField.** - `published` (default) — filter on the work's print or online publication date. Best for "what came out this week." - `created` — filter on when Crossref first indexed the work. Useful when a publisher backdates the print date but the DOI is fresh. **typesFilter.** Restrict to specific Crossref work types. Common values: `journal-article`, `book-chapter`, `proceedings-article`, `posted-content`, `dataset`. Empty = no filter. ### Pricing PAY\_PER\_EVENT · `$0.001 per paper_extracted` · 1 000 papers = $1. ### Buyer - Academic research offices (literature scans). - Bibliometrics / research-impact tools. - Patent firms (prior-art sweep). - Journalists tracking new policy / health / climate publications. - Anyone building a literature-review tool that needs a working daily new-works feed. ### Source `https://api.crossref.org/works` — Crossref's free public REST API. The actor pages with the `cursor=*` deep-pagination protocol so even 100 000-result windows finish without state. UA includes a polite `mailto:` to land on the prioritized API pool. # Actor input Schema ## `queries` (type: `array`): Free-text search terms. Crossref tokenizes — "climate change adaptation" returns works whose title/abstract/authors contain those tokens. One run handles many queries; one row per matched work. ## `sinceDays` (type: `integer`): Server-side date filter. The actor pulls works whose `dateField` falls in \[today - N, today]. ## `dateField` (type: `string`): `published` (default) uses the work's print/online publication date. `created` uses when Crossref first indexed the work — useful when a publisher backdates. ## `maxResultsPerQuery` (type: `integer`): Hard cap per query. The actor uses Crossref's deep-paging cursor so this can be large. ## `typesFilter` (type: `array`): Optional. Common values: `journal-article`, `book-chapter`, `proceedings-article`, `posted-content`, `dataset`. Empty = no filter. ## Actor input object example ```json { "queries": [ "large language models", "diffusion models" ], "sinceDays": 7, "dateField": "published", "maxResultsPerQuery": 1000, "typesFilter": [] } ``` # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "queries": [ "large language models", "diffusion models" ] }; // Run the Actor and wait for it to finish const run = await client.actor("v0iddo/crossref-recent-publications").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`); const { items } = await client.dataset(run.defaultDatasetId).listItems(); items.forEach((item) => { console.dir(item); }); // 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs ``` ## Python example ```python from apify_client import ApifyClient # Initialize the ApifyClient with your Apify API token # Replace '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = { "queries": [ "large language models", "diffusion models", ] } # Run the Actor and wait for it to finish run = client.actor("v0iddo/crossref-recent-publications").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "queries": [ "large language models", "diffusion models" ] }' | apify call v0iddo/crossref-recent-publications --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=v0iddo/crossref-recent-publications", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Crossref Recent Publications by Query", "description": "For each query, pull recent works from Crossref's public REST API with a server-side date filter. One row per work — DOI, title, authors, type, container, ISSN, abstract, refs, citations, license, funders. Covers all academia (humanities, social sci, engineering, business). No auth.", "version": "0.1", "x-build-id": "eSwQEDCDJMP6C6qGq" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/v0iddo~crossref-recent-publications/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-v0iddo-crossref-recent-publications", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/v0iddo~crossref-recent-publications/runs": { "post": { "operationId": "runs-sync-v0iddo-crossref-recent-publications", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/v0iddo~crossref-recent-publications/run-sync": { "post": { "operationId": "run-sync-v0iddo-crossref-recent-publications", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "required": [ "queries" ], "properties": { "queries": { "title": "Search queries", "type": "array", "description": "Free-text search terms. Crossref tokenizes — \"climate change adaptation\" returns works whose title/abstract/authors contain those tokens. One run handles many queries; one row per matched work.", "items": { "type": "string" } }, "sinceDays": { "title": "Window — last N days", "minimum": 1, "maximum": 365, "type": "integer", "description": "Server-side date filter. The actor pulls works whose `dateField` falls in [today - N, today].", "default": 7 }, "dateField": { "title": "Which date to filter on", "enum": [ "published", "created" ], "type": "string", "description": "`published` (default) uses the work's print/online publication date. `created` uses when Crossref first indexed the work — useful when a publisher backdates.", "default": "published" }, "maxResultsPerQuery": { "title": "Max results per query", "minimum": 1, "maximum": 100000, "type": "integer", "description": "Hard cap per query. The actor uses Crossref's deep-paging cursor so this can be large.", "default": 1000 }, "typesFilter": { "title": "Restrict to work types", "type": "array", "description": "Optional. Common values: `journal-article`, `book-chapter`, `proceedings-article`, `posted-content`, `dataset`. Empty = no filter.", "default": [], "items": { "type": "string" } } } }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } } ```